1. Configuración y Carga de Datos

datos_originales <- readr::read_csv2("C:/Users/cordo/OneDrive/Desktop/ESTADISITCA/Oil__Gas____Other_Regulated_Wells__Beginning_1860.csv")
head(datos_originales, 5)
## # A tibble: 5 × 55
##   `API Well Number` `County Code` `API Hole Number` Sidetrack Completion
##               <dbl>         <dbl>             <dbl>     <dbl>      <dbl>
## 1           3.10e15             3              2670         0          0
## 2           3.10e15             3              4599         0          0
## 3           3.10e15             3              4842         0          0
## 4           3.10e15             3              5419         0          0
## 5           3.11e15           101             26525         0          0
## # ℹ 50 more variables: `Well Name` <chr>, `Company Name` <chr>,
## #   `Operator Number` <dbl>, `Well Type` <chr>, `Map Symbol` <chr>,
## #   `Well Status` <chr>, `Status Date` <chr>, `Permit Application Date` <chr>,
## #   `Permit Issued Date` <chr>, `Date Spudded` <chr>,
## #   `Date of Total Depth` <chr>, `Completion Decade` <chr>,
## #   `Completion Year` <chr>, `Completion Month` <chr>, `Completion Day` <chr>,
## #   `Date Well Plugged` <chr>, `Date Well Confidentiality Ends` <chr>, …

2. Extracción y Limpieza de la Variable

datos_limpios <- datos_originales %>%
  select(Slant) %>%
  filter(!is.na(Slant)) %>%
  mutate(Slant = as.character(trimws(Slant)))

head(datos_limpios, 10)
## # A tibble: 10 × 1
##    Slant   
##    <chr>   
##  1 Vertical
##  2 Vertical
##  3 Vertical
##  4 Vertical
##  5 Vertical
##  6 Vertical
##  7 Vertical
##  8 Vertical
##  9 Vertical
## 10 Vertical

3. Identificación de la Variable

  • Variable: SLANT (Inclinación del pozo).
  • Tipo: Cualitativa Nominal.
  • Descripción: Trayectoria del pozo (vertical, direccional u horizontal).

4. Tabla de Distribución de Frecuencias

tdf_slant <- datos_limpios %>%
  group_by(Slant) %>%
  summarise(Fi = n(), .groups = 'drop') %>%
  arrange(desc(Fi)) %>%
  mutate(hi = Fi / sum(Fi), Pi = hi * 100, Fi_ac = cumsum(Fi), hi_ac = cumsum(hi))

tdf_slant %>%
  rename(`Tipo (Slant)` = Slant, `Frec. Absoluta (Fi)` = Fi, `Frec. Relativa (hi)` = hi, 
         `Porcentaje (%)` = Pi, `Frec. Abs. Acumulada (Fi_ac)` = Fi_ac, `Frec. Rel. Acumulada (hi_ac)` = hi_ac) %>%
  kbl(caption = "<center><b>TABLA 1. Distribución de Frecuencias de Pozos por Inclinación</b></center>", 
      align = "lccccc", escape = FALSE) %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = TRUE, html_font = "Lora") %>%
  row_spec(0, background = "#1D4E73", color = "white", bold = TRUE) %>%
  footnote(general = "", general_title = "Fuente: Oil & Gas & Other Regulated Wells - Beginning 1860 ")
TABLA 1. Distribución de Frecuencias de Pozos por Inclinación
Tipo (Slant) Frec. Absoluta (Fi) Frec. Relativa (hi) Porcentaje (%) Frec. Abs. Acumulada (Fi_ac) Frec. Rel. Acumulada (hi_ac)
Vertical 46606 0.9840378 98.4037836 46606 0.9840378
Horizontal 498 0.0105148 1.0514759 47104 0.9945526
Directional 258 0.0054474 0.5447405 47362 1.0000000
Fuente: Oil & Gas & Other Regulated Wells - Beginning 1860

5. Representación Gráfica

5.1 Gráfica N°1 — Diagrama de Barras (Frecuencia Absoluta)

ggplot(tdf_slant, aes(x = reorder(Slant, -Fi), y = Fi, fill = Slant)) +
  geom_bar(stat = "identity", show.legend = FALSE) +
  geom_text(aes(label = Fi), vjust = -0.5, size = 8, family = "Lora") + 
  scale_fill_manual(values = paleta_azul) +
  theme_minimal() + 
  labs(title = "Gráfica N°1: Frecuencia Absoluta", x = "Tipo de Inclinación", y = "Frecuencia Absoluta (Fi)") +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", color = "#1D4E73", size = 28, family = "Lora"),
    axis.title = element_text(size = 22, family = "Lora"), 
    axis.text = element_text(size = 20, family = "Lora")
  )

Gráfica N°2: Distribución Porcentual

ggplot(tdf_slant, aes(x = reorder(Slant, -Pi), y = Pi, fill = Slant)) +
  geom_bar(stat = "identity", show.legend = FALSE) +
  geom_text(aes(label = paste0(round(Pi, 1), "%")), vjust = -0.5, size = 8, family = "Lora") + 
  scale_fill_manual(values = paleta_azul) +
  theme_minimal() + 
  labs(title = "Gráfica N°2: Distribución Porcentual", x = "Tipo de Inclinación", y = "Porcentaje (%)") +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", color = "#1D4E73", size = 28, family = "Lora"),
    axis.title = element_text(size = 22, family = "Lora"), 
    axis.text = element_text(size = 20, family = "Lora")
  )

Gráfica N°3: Diagrama Circular

ggplot(tdf_slant, aes(x = "", y = Pi, fill = Slant)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0) +
  # Usamos un 'ifelse' para solo mostrar etiquetas mayores a 5%
  geom_text(aes(y = cumsum(Pi) - Pi/2, 
                label = ifelse(Pi > 5, paste0(round(Pi, 1), "%"), "")), 
            family = "Lora", size = 8, color = "white", fontface = "bold") + 
  scale_fill_manual(values = paleta_azul) +
  theme_void() +
  labs(title = "Gráfica N°3: Distribución Porcentual") +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", color = "#1D4E73", size = 28, family = "Lora"),
    legend.text = element_text(size = 18, family = "Lora"),
    legend.title = element_text(size = 20, family = "Lora")
  )

6. Tabla de Indicadores

indicadores <- data.frame(
  Indicador = c("Total de Pozos (N)", "Moda (Tipo)", "Porcentaje de la Moda"),
  Valor = c(comma(sum(tdf_slant$Fi)), tdf_slant$Slant[1], paste0(round(tdf_slant$Pi[1], 2), "%"))
)

indicadores %>%
  kbl(caption = "<center><b>TABLA 2. Indicadores Estadísticos</b></center>", align = "lc", escape = FALSE) %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = TRUE, html_font = "Lora") %>%
  row_spec(0, background = "#2870A4", color = "white", bold = TRUE) %>%
  footnote(general = "", general_title = "Autor: Jennifer Cordones ")
TABLA 2. Indicadores Estadísticos
Indicador Valor
Total de Pozos (N) 47,362
Moda (Tipo) Vertical
Porcentaje de la Moda 98.4%
Autor: Jennifer Cordones

7. Conclusión

El análisis realizado sobre la variable SLANT permite destacar:

  • La trayectoria Vertical es la más predominante, representando el 98.4% del total.
  • La estandarización en la inclinación de los pozos indica un diseño operativo consistente en la cuenca, lo cual es fundamental para la planificación técnica.